When is Tree Search Useful for LLM Planning? It Depends on the Discriminator (2024.acl-long)
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| Challenge: | Existing methods to build language agents that can plan efficiently and accurately have not met the needs of advanced planning methods to achieve such improvements. |
| Approach: | They propose to use iterative correction and tree search to solve multi-step problems in a language agent framework with three components: a generator, a discriminator, and a planning method. |
| Outcome: | The proposed methods improve performance on two tasks, text-to-SQL parsing and mathematical reasoning, while using discriminators with 90% accuracy. |
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| Challenge: | Large Language Models excel in various natural language tasks but struggle with long-horizon planning problems requiring structured reasoning. |
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| Challenge: | a recent study shows that large language models (LLMs) are limited in understanding natural language preferences. |
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| Challenge: | Existing studies have focused on developing LLMs to automate complex planning tasks. |
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| Challenge: | Existing studies have shown that LLMs struggle to generate valid plans in the automated planning domain due to weak System 2 competencies. |
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